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Title: Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection
Authors: Chen, Zhaomin
Yeo, Chai Kiat
Lee, Bu Sung
Lau, Chiew Tong
Jin, Yaochu
Keywords: Engineering::Computer science and engineering
Image Outlier Detection
Issue Date: 2018
Source: Chen, Z., Yeo, C. K., Lee, B. S., Lau, C. T., & Jin, Y. (2018). Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing, 309, 192-200. doi:10.1016/j.neucom.2018.05.012
Series/Report no.: Neurocomputing
Abstract: Image outlier detection has been an important research issue for many computer vision tasks. However, most existing outlier detection methods fail in the high-dimensional image datasets. In order to address this problem, we propose a novel image outlier detection method by combining autoencoder with Adaboost (ADAE). By ensembling many weak autoencoders, our method can better capture the statistical correlations among the features of normal data than the single autoencoder. Therefore, the proposed ADAE is able to determine the outliers efficiently. In order to reduce the many parameters in ADAE, we introduce the Sparse Group Lasso (SGL) constraint into the learning objective of ADAE. We combine Adagrad with Proximal Gradient Descent to optimize this additional learning objective. We also propose the multi-objective evolutionary algorithm to determine the best penalty factors of SGL. By evaluating on several famous image datasets, the detection results testify to the outstanding outlier detection performance of ADAE. The evaluation results also show SGL can make the detection model more compact while maintaining the similar detection performance.
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.05.012
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Neurocomputing and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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